OpenRaiser/Intern-Atlas huggingface dataset:
Towards Decentralised Cloud Storage with IPFS: Opportunities, Challenges, and Future Directions
Venue: arXiv cs.NI 2022
Year: 2022

The InterPlanetary File System (IPFS) is a novel decentralised storage architecture, which attempts to provide decentralised cloud storage by building on founding principles of P2P networking and content addressing. IPFS is used by more than 230k peers per week and serves tens of millions of requests per day, which makes it an interesting large-scale operational network to study. While it is used as a building block in several projects and studies, its inner workings, properties, and implications have only been marginally explored in research. Thus, we provide an overview of the IPFS design and its core features, along with the opportunities that it opens as well as the challenges that it faces because of its properties. Overall, IPFS presents an interesting set of characteristics and offers lessons which can help building decentralised systems of the future.
Reverse Back Propagation to Make Full Use of Derivative
Venue: arXiv cs.LG 2022
Year: 2022

The development of the back-propagation algorithm represents a landmark in neural networks. We provide an approach that conducts the back-propagation again to reverse the traditional back-propagation process to optimize the input loss at the input end of a neural network for better effects without extra costs during the inference time. Then we further analyzed its principles and advantages and disadvantages, reformulated the weight initialization strategy for our method. And experiments on MNIST, CIFAR10, and CIFAR100 convinced our approaches could adapt to a larger range of learning rate and learn better than vanilla back-propagation.
Hybrid continuum-discrete macro modelling of multi ring masonry arch bridges
Venue: arXiv cs.CE 2022
Year: 2022

This paper presents a hybrid continuum discrete macro modelling strategy with a multiscale calibration procedure for realistic simulations of brick masonry bridges. The response of these structures is affected by the intrinsic nonlinearity of the masonry material, which in turn depends upon the mechanical properties of units and mortar joints and the bond characteristics. Finite element approaches based upon homogenised representations are widely employed to assess the nonlinear behaviour up to collapse, as they are generally associated with a limited computational demand. However, such models require an accurate calibration of model material parameters to properly allow for masonry bond. According to the proposed approach, the macroscale material parameters are determined by an advanced multi objective strategy with genetic algorithms from the results of mesoscale virtual tests through the minimisation of appropriate functionals of the scale transition error. The developed continuum discrete finite element macroscale description and the calibration procedure are applied to simulate the nonlinear behaviour up to collapse of multi ring arch bridge specimens focusing on the 2D planar response. The results obtained are compared to those achieved using detailed mesoscale models confirming the effectiveness and accuracy of the proposed approach for realistic nonlinear simulations of masonry arch bridges.
Optimal lofted B-spline surface interpolation based on serial closed contours
Venue: arXiv cs.GR 2022
Year: 2022

Modern shape design and capture techniques often lead to the geometric data presented in the form of serial rows of data points. In general, the number of data points varies from row to row. Lofted or skinned B-spline surface interpolation is a technique that generates a B-spline surface that passes through these data points precisely. The traditional process often causes a large increase in the number of control points of the resulting B-spline surface. Much of the work to date in mitigating the effects of this increase has been restricted to open section-curves. The lofting of sequential closed contours using the interpolation technique has not been addressed in the existing literature. In this paper, we present two novel conjectures relating to closed B-spline curve interpolation. We derive the equivalent closed B-spline interpolation condition of the well-established Schoenberg-Whitney condition for open B-spline interpolation, a condition that the parameter values and the domain knots should satisfy to guarantee the system matrix is always invertible or full-rank. We then apply the interpolation condition to the problem of lofted B-spline surface interpolation to serial closed contours. The correctness of these conjectures is validated via numerical results and several practical experiments. Github repository https://github.com/ShutaoTang/LBSI-Project
Non-fungible Tokens: Promise or Peril?
Venue: arXiv cs.CR 2022
Year: 2022

Non-fungible tokens or NFTs are the digital assets on a blockchain. NFTs are unique and they cannot be divided like cryptocurrencies. NFTs could store digital ownership of an artwork or collections or can be fan tokens or tickets for clubs. NFTs are based on a smart contract on a blockchain network which supports them, such as Ethereum, Cardano or Polkadot. Most of the NFTs are now minted on Ethereum (ERC-20) network, but it has some main issues like high transaction fees and low speed. There are lots of domains which can be benefited from NFT technology such as art, music, gaming, sport and wildlife conservation. NFTs could be also bought or sold on lots of NFT marketplaces such as OpenSea and Chiliz. The trend is in a huge hype because the market cap and popularity of NFTs are growing significantly.
Exactly Optimal Deterministic Radio Broadcasting with Collision Detection
Venue: arXiv cs.DC 2022
Year: 2022

We consider the broadcast problem in synchronous radio broadcast models with collision detection. One node of the network is given a message that must be learned by all nodes in the network. We provide a deterministic algorithm that works on the beeping model, which is a restricted version of the radio broadcast model with collision detection. This algorithm improves on the round complexity of previous algorithms. We prove an exactly matching lower bound in the radio broadcast model with collision detection. This shows that the extra power provided by the radio broadcast model with collision detection does not help improve the round complexity.
Giving the Right Answer: a Brief Overview on How to Extend Ranking and Skyline Queries
Venue: arXiv cs.DB 2022
Year: 2022

To retrieve the best results in a database we use Top-K queries and Skyline queries but some problems arise. The formers rely too much on user preferences, which are difficult to quantify and may skew the fetching of the data, while the latters tend to output too much data. In this paper, we explore three different branches of research that seek to overcome such limitations: Flexible/Restricted Skylines, Skyline Ordering/Ranking, and Regret Minimization. We analyze how they work and we make comparisons among them to guide the reader to choose the approach that best fits their use cases.
Fast algorithm for overcomplete order-3 tensor decomposition
Venue: arXiv cs.LG 2022
Year: 2022

We develop the first fast spectral algorithm to decompose a random third-order tensor over $\mathbb{R}^d$ of rank up to $O(d^{3/2}/\text{polylog}(d))$. Our algorithm only involves simple linear algebra operations and can recover all components in time $O(d^{6.05})$ under the current matrix multiplication time. Prior to this work, comparable guarantees could only be achieved via sum-of-squares [Ma, Shi, Steurer 2016]. In contrast, fast algorithms [Hopkins, Schramm, Shi, Steurer 2016] could only decompose tensors of rank at most $O(d^{4/3}/\text{polylog}(d))$. Our algorithmic result rests on two key ingredients. A clean lifting of the third-order tensor to a sixth-order tensor, which can be expressed in the language of tensor networks. A careful decomposition of the tensor network into a sequence of rectangular matrix multiplications, which allows us to have a fast implementation of the algorithm.
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
Venue: arXiv cs.LG 2022
Year: 2022

This paper proposes a class of neural ordinary differential equations parametrized by provably input-to-state stable continuous-time recurrent neural networks. The model dynamics are defined by construction to be input-to-state stable (ISS) with respect to an ISS-Lyapunov function that is learned jointly with the dynamics. We use the proposed method to learn cheap-to-simulate behavioral models for electronic circuits that can accurately reproduce the behavior of various digital and analog circuits when simulated by a commercial circuit simulator, even when interconnected with circuit components not encountered during training. We also demonstrate the feasibility of learning ISS-preserving perturbations to the dynamics for modeling degradation effects due to circuit aging.
Splitting numerical integration for matrix completion
Venue: arXiv cs.LG 2022
Year: 2022

Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical gradient descent within the framework of optimization on manifolds. In particular, we reformulate an unconstrained optimization problem on a low-rank manifold into a differential dynamic system. We develop a splitting numerical integration method by applying a splitting integration scheme to the dynamic system. We conduct the convergence analysis of our splitting numerical integration algorithm. It can be guaranteed that the error between the recovered matrix and true result is monotonically decreasing in the Frobenius norm. Moreover, our splitting numerical integration can be adapted into matrix completion scenarios. Experimental results show that our approach has good scalability for large-scale problems with satisfactory accuracy
Semantic Matching from Different Perspectives
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we pay attention to the issue which is usually overlooked, i.e., \textit{similarity should be determined from different perspectives}. To explore this issue, we release a Multi-Perspective Text Similarity (MPTS) dataset, in which sentence similarities are labeled from twelve perspectives. Furthermore, we conduct a series of experimental analysis on this task by retrofitting some famous text matching models. Finally, we obtain several conclusions and baseline models, laying the foundation for the following investigation of this issue. The dataset and code are publicly available at Github\footnote{\url{https://github.com/autoliuweijie/MPTS}
Source Code Summarization with Structural Relative Position Guided Transformer
Venue: arXiv cs.CL 2022
Year: 2022

Source code summarization aims at generating concise and clear natural language descriptions for programming languages. Well-written code summaries are beneficial for programmers to participate in the software development and maintenance process. To learn the semantic representations of source code, recent efforts focus on incorporating the syntax structure of code into neural networks such as Transformer. Such Transformer-based approaches can better capture the long-range dependencies than other neural networks including Recurrent Neural Networks (RNNs), however, most of them do not consider the structural relative correlations between tokens, e.g., relative positions in Abstract Syntax Trees (ASTs), which is beneficial for code semantics learning. To model the structural dependency, we propose a Structural Relative Position guided Transformer, named SCRIPT. SCRIPT first obtains the structural relative positions between tokens via parsing the ASTs of source code, and then passes them into two types of Transformer encoders. One Transformer directly adjusts the input according to the structural relative distance; and the other Transformer encodes the structural relative positions during computing the self-attention scores. Finally, we stack these two types of Transformer encoders to learn representations of source code. Experimental results show that the proposed SCRIPT outperforms the state-of-the-art methods by at least 1.6%, 1.4% and 2.8% with respect to BLEU, ROUGE-L and METEOR on benchmark datasets, respectively. We further show that how the proposed SCRIPT captures the structural relative dependencies.
Analog Physical-Layer Relay Attacks with Application to Bluetooth and Phase-Based Ranging
Venue: arXiv cs.CR 2022
Year: 2022

Today, we use smartphones as multi-purpose devices that communicate with their environment to implement context-aware services, including asset tracking, indoor localization, contact tracing, or access control. As a de-facto standard, Bluetooth is available in virtually every smartphone to provide short-range wireless communication. Importantly, many Bluetooth-driven applications such as Phone as a Key (PaaK) for vehicles and buildings require proximity of legitimate devices, which must be protected against unauthorized access. In earlier access control systems, attackers were able to violate proximity-verification through relay station attacks. However, the vulnerability of Bluetooth against such attacks was yet unclear as existing relay attack strategies are not applicable or can be defeated through wireless distance measurement. In this paper, we design and implement an analog physical-layer relay attack based on low-cost off-the-shelf radio hardware to simultaneously increase the wireless communication range and manipulate distance measurements. Using our setup, we successfully demonstrate relay attacks against Bluetooth-based access control of a car and a smart lock. Further, we show that our attack can arbitrarily manipulate Multi-Carrier Phase-based Ranging (MCPR) while relaying signals over 90 m.
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme
Venue: arXiv cs.NE 2022
Year: 2022

Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, recurrent layers are executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we observe that the output of a neuron exhibits small changes in consecutive invocations.~We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches each neuron's output and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 26.7\% of computations, resulting in 21\% energy savings and 1.4x speedup on average.
Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection
Venue: arXiv cs.LG 2022
Year: 2022

Graph neural networks (GNNs) have been widely applied to numerous fields. A recent work which combines layered structure and residual connection proposes an improved deep architecture to extend CAmouflage-REsistant GNN (CARE-GNN) to deep models named as Residual Layered CARE-GNN (RLC-GNN), which forms self-correcting and incremental learning mechanism, and achieves significant performance improvements on fraud detection task. However, we spot three issues of RLC-GNN, which are the usage of neighboring information reaching limitation, the training difficulty which is inherent problem to deep models and lack of comprehensive consideration about node features and external patterns. In this work, we propose three approaches to solve those three problems respectively. First, we suggest conducting similarity measure via cosine distance to take both local features and external patterns into consideration. Then, we combine the similarity measure module and the idea of adjacency-wise normalization with node-wise and batch-wise normalization and then propound partial neighborhood normalization methods to overcome the training difficulty while mitigating the impact of too much noise caused by high-density of graph. Finally, we put forward intermediate information supplement to solve the information limitation. Experiments are conducted on Yelp and Amazon datasets. And the results show that our proposed methods effectively solve the three problems. After applying the three methods, we achieve 4.81%, 6.62% and 6.81% improvements in the metrics of recall, AUC and Macro-F1 respectively on the Yelp dataset. And we obtain 1.65% and 0.29% improvements in recall and AUC respectively on the Amazon datasets.
What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
Venue: arXiv cs.LG 2022
Year: 2022

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics.
On the Importance of Building High-quality Training Datasets for Neural Code Search
Venue: arXiv cs.SE 2022
Year: 2022

The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query and code pairs is demanded to establish a precise mapping from the natural language to the programming language. Due to the limited availability, most widely-used code search datasets are established with compromise, such as using code comments as a replacement of queries. Our empirical study on a famous code search dataset reveals that over one-third of its queries contain noises that make them deviate from natural user queries. Models trained through noisy data are faced with severe performance degradation when applied in real-world scenarios. To improve the dataset quality and make the queries of its samples semantically identical to real user queries is critical for the practical usability of neural code search. In this paper, we propose a data cleaning framework consisting of two subsequent filters: a rule-based syntactic filter and a model-based semantic filter. This is the first framework that applies semantic query cleaning to code search datasets. Experimentally, we evaluated the effectiveness of our framework on two widely-used code search models and three manually-annotated code retrieval benchmarks. Training the popular DeepCS model with the filtered dataset from our framework improves its performance by 19.2% MRR and 21.3% Answer@1, on average with the three validation benchmarks.
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
Venue: arXiv cs.CL 2022
Year: 2022

Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided into ones that require training - supervised and ones that do not - unsupervised. In this study, we are interested in settings, where for a language under investigation, no training data is available. More specifically, we explore whether pretrained multilingual language models can be employed for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data and whether they outperform state-of-the-art unsupervised keyword extractors. The comparison is conducted on six news article datasets covering two high-resource languages, English and Russian, and four low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i.e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.
Client Monitoring Software: A Monitoring Tool for Greatleaf Land Inc
Venue: arXiv cs.HC 2022
Year: 2022

Monitoring typically supports greater analysis and allows for a lot deeper data collection on a Web browser level. Analysts may usually see the use of web-based monitoring software within an entire client context when it comes to client-side monitoring, on the other hand. In the case of Greatleaf Land Inc., their monitoring is somehow traditional. Traditional means that the paper method of monitoring is being utilized. When arranging piles of paper and afterwards forgetting about it, users lose track of where the information is situated. Another notable issue is that some information situating on a written on paper isn't easily cant be detected beyond skimming, which is a process normally being used to fast track records on a flipping pages, and sometimes there's also the fact that written records situating on papers can only situate in one location at a certain time unless the custodian has given up and made copies for everything. This study will check the feasibility of a web-based client monitoring software for Greatleaf Land Inc. By using ISO 25010, the study will determine if it is beneficial for Greatleaf Land Inc. to have an online monitoring software rather than settling to the traditional method that is currently being used by their company.